An ocean is an activity space of an underwater vehicle, and thermocline, internal waves and other meso- and micro-scale ocean processes in the ocean directly threat the navigation safety of the underwater vehicle and affect the smooth implementation of its tasks. Thus, real-time construction of a navigation environment field around the underwater vehicle and rapid diagnosis of the meso-micro scale ocean processes have important significance. Currently, the marine environment data are mainly obtained in two ways, one way is to calculate by a numerical model, but the data can only approximately reflect the regular features of the marine environment field and are difficult to extract the meso-micro scale ocean processes; and the other way is to directly measure by a sensor, such data are the most direct observation with higher precision to the marine environment field and contain important marine information. However, due to the limitation of observation equipment and the random variation of physical quantity of an observation point, an observation result has unavoidable systematic errors and random errors. Thus, the data obtained in the two ways have their own advantages and disadvantages.
A data assimilation method is a method combining observation data with a theoretical model, which is developed with the development of numerical calculation and numerical forecast business. It combines observation data obtained by different observation measures in different spaces, different times and with a mathematical model according to a certain optimization criteria and method, and establishes an optimal relation in which the model and the data coordinate with each other. By means of the data assimilation technology, effective information contained in the observation data can be maximally extracted to enhance and improve the performance of an analysis and forecast system. Therefore, the real-time observation data are combined with a theoretical model result by data assimilation to absorb the both advantages, and thus an approximately actual marine environment state field can be constructed. Three-dimensional variation is a common ocean data assimilation method. In the three-dimensional variation assimilation, a background field error covariance matrix plays an important role, which determines the degree of correction of an analysis field relative to a background field. One of the main problems researched by the traditional three-dimensional variation assimilation method is how to better construct the background field error covariance matrix. At present, there are mainly two three-dimensional variation assimilation methods, one is a correlation length method, and the other is a recursive filtering method. The correlation length method is used for constructing the background field error covariance matrix. However, the method has great randomness in the practical application, and it is difficult to give an accurate estimate on the relevant scale. Meanwhile, the method requires a huge internal memory and a huge calculation overhead. The recursive filtering method does not require explicit construction of the error covariance matrix, and compared with the correlation length method, the recursive filtering method has the advantages of high calculation speed and capability of saving the internal memory. But the correlation length method and the recursive filtering method have common defects, that is, it is difficult to effectively extract the multi-scale observation information. Thus, for the demands of the underground navigation of the underwater vehicle on the surrounding marine environment field, an efficient actually-measured marine environment data assimilation method is designed, which has an important practical value on the real-time construction of an underwater environment state field and the fast extraction of a multi-scale ocean process.